Cross-Platform Budget Allocator
SKILL.md
HOW_TO_USE.md
sample_input.json
expected_output.json
skillscross-platformSKILL.md
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# Cross-Platform Budget Allocator
Stop guessing. Start allocating with data.
## Core Philosophy
**Every dollar should earn its place.** Platform loyalty is expensive, money flows to performance.
**The Allocation Paradox:** Your best platform today might not be your best platform at 2x the budget. Efficiency curves are different for every channel.
**The Three Principles of Allocation:**
1. **Marginal returns matter:** $1,000 more to Google might yield less than $1,000 to Meta
2. **Diminishing returns are real:** Every platform has an efficiency ceiling
3. **Portfolio thinking wins:** Diversification protects against platform volatility
---
## Required Context
### Must Have
**1. Platform Performance Data**
For each platform currently running:
- Monthly spend
- Conversions (or revenue)
- CPA (or ROAS)
- Trend direction (improving/stable/declining)
**2. Budget Parameters**
- Total budget to allocate
- Time period (monthly/quarterly)
- Flexibility (fixed total vs. range)
**3. Business Goals**
- Primary KPI (CPA, ROAS, volume, brand awareness)
- Target efficiency metrics
- Growth vs. efficiency priority
### Strongly Recommended
**4. Platform-Specific Context**
- Impression share / audience saturation data
- Historical performance at different spend levels
- Learning phase or scaling status
**5. Constraints**
- Platform minimums (can't go below $X)
- Platform maximums (caps)
- New platform testing budget
- Brand vs. non-brand split requirements
### Nice to Have
- Attribution model being used
- Customer journey insights (which platform drives awareness vs. conversion)
- Competitive landscape by platform
- Seasonality patterns
- Previous allocation experiments
---
## Allocation Framework
### Step 1: Performance Baseline
**Efficiency Ranking:**
Rank platforms by primary KPI (CPA or ROAS)
| Rank | Platform | CPA | vs. Target | Trend |
|------|----------|-----|------------|-------|
| 1 | [Best] | $X | -X% | [→↑↓] |
| 2 | [Second] | $X | X% | [→↑↓] |
| ... | ... | ... | ... | ... |
**Volume vs. Efficiency Matrix:**
```
HIGH EFFICIENCY
│
SCALE │ MAXIMIZE
(High volume, │ (High volume,
good efficiency) │ great efficiency)
│
──────────────────────┼──────────────────────
│
TEST/CUT │ NICHE
(Low volume, │ (Low volume,
poor efficiency) │ great efficiency)
│
LOW EFFICIENCY
```
---
### Step 2: Marginal Return Analysis
**Key Question:** If I add $1,000 to each platform, what do I get?
**Estimating Marginal CPA:**
- Platform at <50% impression share: Likely similar CPA on incremental spend
- Platform at 50-70% impression share: Expect 10-20% CPA increase
- Platform at >70% impression share: Expect 20-40% CPA increase
- Platform in "Learning Limited": May improve with more budget
**Efficiency Curve Patterns:**
```
CPA
│
│ ╭────── Saturated
│ ╭────╯
│ ╭────╯
│ ╭────╯
│ ╭────╯
│ ╭────╯
│╭────╯
└─────────────────────────────────────── Spend
Sweet Spot Diminishing Ceiling
```
---
### Step 3: Allocation Models
**Model A: Efficiency-Weighted Allocation**
Allocate proportionally to efficiency (inverse of CPA)
```
Platform Share = (1/Platform CPA) / Sum(1/All CPAs)
```
Best for: Maximizing total conversions at target efficiency
**Model B: Volume-Weighted with Efficiency Floor**
Allocate to platforms meeting efficiency threshold, weighted by current volume
```
If CPA < Target: Eligible for budget
Platform Share = Platform Volume / Total Eligible Volume
```
Best for: Scaling while maintaining efficiency standards
**Model C: Marginal Return Optimization**
Allocate incremental dollars to platform with best marginal return
```
1. Start with minimum allocations
2. Add $1K to platform with best projected marginal CPA
3. Repeat until budget exhausted
```
Best for: Maximizing efficiency at any budget level
**Model D: Portfolio Diversification**
Set minimum/maximum caps to ensure diversification
```
No platform > 50% of total budget
No platform < 10% of total budget (if running)
Testing budget: 10-15% to new/experimental platforms
```
Best for: Risk management and platform dependency reduction
---
### Step 4: Constraint Application
**Common Constraints:**
| Constraint Type | How to Handle |
|-----------------|---------------|
| Platform minimum | Set floor, allocate remaining optimally |
| Platform maximum | Set ceiling, reallocate overflow to next best |
| Brand budget | Carve out first, optimize non-brand separately |
| Testing budget | Reserve %, don't include in optimization |
| Seasonal adjustment | Apply multipliers to base allocation |
---
### Step 5: Scenario Modeling
**Always model three scenarios:**
1. **Conservative:** Prioritize proven performers, minimal change
2. **Balanced:** Optimize based on data, moderate reallocation
3. **Aggressive:** Chase highest marginal returns, significant shifts
---
## Platform-Specific Considerations
### Google Ads
- **Strengths:** High intent, measurable, scalable
- **Scaling signals:** Impression share <80%, budget limited campaigns
- **Watch for:** CPC inflation in competitive auctions
- **Typical efficiency curve:** Gradual decline after 70% IS
### Meta Ads
- **Strengths:** Broad reach, creative-driven, discovery
- **Scaling signals:** Frequency <3, audience not exhausted
- **Watch for:** Creative fatigue, iOS attribution gaps
- **Typical efficiency curve:** Sharp decline when frequency spikes
### TikTok Ads
- **Strengths:** Low CPMs, engaged audience, viral potential
- **Scaling signals:** New platform opportunity, creative performing
- **Watch for:** Conversion tracking maturity, audience fit
- **Typical efficiency curve:** Volatile, creative-dependent
### LinkedIn Ads
- **Strengths:** B2B targeting precision, professional context
- **Scaling signals:** Audience size >50K, frequency manageable
- **Watch for:** High CPMs ($8-15), small audiences exhaust fast
- **Typical efficiency curve:** Quick saturation in niche audiences
### Microsoft Ads
- **Strengths:** Lower CPCs, older demographic, desktop heavy
- **Scaling signals:** Google campaigns profitable, IS headroom
- **Watch for:** Lower volume, import quality issues
- **Typical efficiency curve:** Similar to Google, lower ceiling
---
## Output Format
### Budget Allocation Summary
```
═══════════════════════════════════════════════════════════════
CROSS-PLATFORM BUDGET ALLOCATION
Total Budget: $[X]/month
Primary Goal: [CPA/ROAS/Volume]
Allocation Model: [Model Used]
═══════════════════════════════════════════════════════════════
```
---
### Current State Analysis
**Performance by Platform:**
| Platform | Current Spend | Conversions | CPA | vs. Target | ROAS | Trend |
|----------|--------------|-------------|-----|------------|------|-------|
| Google Ads | $[X] | [X] | $[X] | [X]% | [X] | [→↑↓] |
| Meta Ads | $[X] | [X] | $[X] | [X]% | [X] | [→↑↓] |
| [Platform] | $[X] | [X] | $[X] | [X]% | [X] | [→↑↓] |
| **Total** | $[X] | [X] | $[X] | [X]% | [X] | |
**Current Allocation:**
```
Google Ads: ████████████████████ [X]% ($[X])
Meta Ads: ████████████ [X]% ($[X])
[Platform]: ████ [X]% ($[X])
```
**Efficiency Ranking:** [Platform 1] > [Platform 2] > [Platform 3]
**Scaling Headroom Assessment:**
| Platform | Saturation Signal | Headroom | Notes |
|----------|-------------------|----------|-------|
| Google | [X]% impression share | [High/Med/Low] | [Notes] |
| Meta | [X] frequency | [High/Med/Low] | [Notes] |
| [Platform] | [Signal] | [High/Med/Low] | [Notes] |
---
### Recommended Allocation
**Model Used:** [Model Name]
**Rationale:** [One sentence explanation]
| Platform | Current | Recommended | Change | % of Total |
|----------|---------|-------------|--------|------------|
| Google Ads | $[X] | $[X] | [+/-$X] | [X]% |
| Meta Ads | $[X] | $[X] | [+/-$X] | [X]% |
| [Platform] | $[X] | $[X] | [+/-$X] | [X]% |
| Testing/New | $[X] | $[X] | [+/-$X] | [X]% |
| **Total** | $[X] | $[X] | | 100% |
**Visual Comparison:**
```
CURRENT RECOMMENDED
Google: ████████████ 40% Google: ██████████████ 45%
Meta: ██████████ 35% Meta: ████████ 30%
TikTok: ████ 15% TikTok: ██████ 20%
Other: ██ 10% Other: █ 5%
```
---
### Allocation Rationale
**Why increase [Platform]:**
- [Reason 1 with data]
- [Reason 2 with data]
**Why decrease [Platform]:**
- [Reason 1 with data]
- [Reason 2 with data]
**Why maintain [Platform]:**
- [Reason with data]
---
### Projected Outcomes
**At Recommended Allocation:**
| Metric | Current | Projected | Change |
|--------|---------|-----------|--------|
| Total Conversions | [X] | [X] | [+/-X]% |
| Blended CPA | $[X] | $[X] | [+/-X]% |
| Blended ROAS | [X] | [X] | [+/-X]% |
**Assumptions:**
- [Assumption 1]
- [Assumption 2]
- [Assumption 3]
**Confidence Level:** [High/Medium/Low]
- High: Historical data supports projections
- Medium: Some extrapolation required
- Low: Significant unknowns
---
### Scenario Comparison
| Scenario | Google | Meta | [Other] | Projected CPA | Projected Volume |
|----------|--------|------|---------|---------------|------------------|
| Conservative | $[X] | $[X] | $[X] | $[X] | [X] |
| **Balanced (Rec)** | $[X] | $[X] | $[X] | $[X] | [X] |
| Aggressive | $[X] | $[X] | $[X] | $[X] | [X] |
**Conservative:** [Description]
**Balanced:** [Description]
**Aggressive:** [Description]
---
### Implementation Plan
**Phase 1: Immediate Changes (Week 1)**
- [ ] [Specific budget change]
- [ ] [Specific budget change]
**Phase 2: Gradual Shift (Weeks 2-4)**
- [ ] [Gradual adjustment]
- [ ] [Gradual adjustment]
**Phase 3: Optimization (Week 4+)**
- [ ] Review performance at new levels
- [ ] Fine-tune based on actual results
**Change Management:**
- Don't shift >25% of any platform's budget at once
- Allow 2 weeks for algorithms to adjust
- Monitor daily for first week after changes
---
### Monitoring & Rebalancing
**Weekly Check:**
- [ ] CPA by platform vs. projection
- [ ] Spend pacing vs. allocation
- [ ] Any platform hitting constraints
**Rebalancing Triggers:**
- Platform CPA exceeds projection by >20% for 2 weeks
- Platform underdelivering by >15% for 2 weeks
- Major external change (algorithm update, seasonality shift)
**Quarterly Review:**
- Full reallocation analysis
- Update efficiency curves
- Reassess platform priorities
---
### Risks & Mitigation
**Risk 1: [Platform] underperforms at higher spend**
- Likelihood: [X]
- Impact: [X]
- Mitigation: Gradual scaling, weekly monitoring, quick pullback plan
**Risk 2: [Platform] can't absorb budget decrease**
- Likelihood: [X]
- Impact: [X]
- Mitigation: Reallocate gradually, maintain campaigns for quick scale-back
**Risk 3: Attribution differences distort comparison**
- Likelihood: [X]
- Impact: [X]
- Mitigation: Use consistent windows, acknowledge limitations, focus on trends
---
## Limitations
**I can provide:**
- Data-driven allocation recommendations
- Scenario modeling
- Implementation guidance
- Monitoring framework
**I cannot provide:**
- Cross-platform attribution modeling
- Incrementality measurement
- Platform-specific optimization tactics
- Creative strategy by platform
**For better allocations, also provide:**
- Historical performance at different spend levels
- Impression share / saturation data
- Customer journey insights
- Attribution window consistency info
---
## Quality Checklist
Before delivering allocation:
- [ ] All platforms included with current performance
- [ ] Allocation sums to total budget
- [ ] Rationale provided for each change
- [ ] Constraints respected
- [ ] Projections include assumptions
- [ ] Implementation plan is gradual (not shock changes)
- [ ] Monitoring framework included
- [ ] Risks identified with mitigations
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# How to Use This Skill
Hey Claude, I just added the "cross-platform-budget-allocator" skill. Can you help me figure out how to split my ad budget across Google, Meta, and TikTok?
## Example Invocations
**Example 1: Full Allocation Analysis**
Hey Claude, I just added the "cross-platform-budget-allocator" skill. I have $50K/month to spend across Google Ads, Meta Ads, and LinkedIn. Here's my performance by platform. How should I allocate?
**Example 2: Budget Increase**
Hey Claude, I just added the "cross-platform-budget-allocator" skill. My budget is increasing from $30K to $45K next month. I currently run Google and Meta. Should I add a new channel or scale existing ones?
**Example 3: Rebalancing**
Hey Claude, I just added the "cross-platform-budget-allocator" skill. Meta performance has dropped 30% in the last month while Google stayed flat. Should I shift budget? How much?
**Example 4: New Platform Decision**
Hey Claude, I just added the "cross-platform-budget-allocator" skill. I'm considering adding TikTok to my mix. How much should I test with, and where should that budget come from?
**Example 5: Efficiency vs. Volume**
Hey Claude, I just added the "cross-platform-budget-allocator" skill. I need to hit 500 conversions next month but also keep CPA under $50. Here's my platform data, how do I balance volume and efficiency?
## What to Provide
**Required:**
- Platforms you're currently running (or considering)
- Monthly spend per platform
- Conversions (or revenue) per platform
- CPA or ROAS per platform
- Total budget to allocate
- Primary goal (efficiency vs. volume)
**Recommended:**
- Target CPA or ROAS
- Trend direction per platform (improving/declining/stable)
- Any saturation signals (impression share, frequency)
- Constraints (minimums, maximums, brand carve-outs)
**Optional:**
- Historical performance at different spend levels
- Attribution setup/windows
- Customer journey insights
- Seasonality patterns
- Competitive context
## What You'll Get
1. **Current State Analysis** - Performance comparison across platforms
2. **Efficiency Ranking** - Which platforms deserve more budget
3. **Recommended Allocation** - Specific dollar amounts per platform
4. **Rationale** - Why each change is recommended
5. **Projected Outcomes** - Expected CPA/ROAS/volume at new allocation
6. **Scenario Comparison** - Conservative/Balanced/Aggressive options
7. **Implementation Plan** - How to make changes gradually
8. **Monitoring Framework** - When to rebalance
## Pro Tips
- Provide performance data for the same time period across all platforms
- Include impression share or frequency data, it reveals scaling headroom
- Be clear about constraints (can't reduce Platform X below $Y)
- If attribution differs by platform, mention it, affects comparisons
- Budget shifts should be gradual (25% max at a time), algorithms need time to adjust
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{
"business_context": {
"company": "B2B SaaS - HR Software",
"conversion_type": "Demo Requests",
"target_cpa": 150,
"average_deal_value": 12000,
"sales_capacity": "Can handle up to 200 demos/month"
},
"budget_parameters": {
"total_monthly_budget": 60000,
"budget_flexibility": "Fixed total, flexible allocation",
"planning_period": "Q1 2025",
"testing_budget_preference": "10-15% for experiments"
},
"current_allocation": {
"google_ads": {
"monthly_spend": 30000,
"conversions": 180,
"cpa": 166.67,
"roas": null,
"impression_share": 68,
"trend": "stable",
"campaign_types": [
"Search - Brand",
"Search - Non-Brand",
"Performance Max"
],
"notes": "Non-brand struggling, brand efficient, PMax inconsistent"
},
"meta_ads": {
"monthly_spend": 20000,
"conversions": 95,
"cpa": 210.53,
"roas": null,
"frequency": 2.8,
"trend": "declining",
"campaign_types": [
"Prospecting - Lookalikes",
"Retargeting"
],
"notes": "Was better 3 months ago, creative fatigue suspected"
},
"linkedin_ads": {
"monthly_spend": 10000,
"conversions": 55,
"cpa": 181.82,
"roas": null,
"frequency": 3.5,
"trend": "stable",
"campaign_types": [
"Sponsored Content",
"Lead Gen Forms"
],
"notes": "Smaller volume but decent quality leads, audience getting saturated"
}
},
"platform_details": {
"google_ads": {
"brand_spend": 8000,
"brand_conversions": 75,
"brand_cpa": 106.67,
"nonbrand_spend": 15000,
"nonbrand_conversions": 72,
"nonbrand_cpa": 208.33,
"pmax_spend": 7000,
"pmax_conversions": 33,
"pmax_cpa": 212.12,
"search_impression_share": 68,
"budget_limited_campaigns": 2
},
"meta_ads": {
"prospecting_spend": 14000,
"prospecting_conversions": 55,
"prospecting_cpa": 254.55,
"retargeting_spend": 6000,
"retargeting_conversions": 40,
"retargeting_cpa": 150,
"top_audience": "LAL - Customers 1%",
"audience_size": "1.2M"
},
"linkedin_ads": {
"sponsored_content_spend": 7000,
"sponsored_content_conversions": 35,
"sponsored_content_cpa": 200,
"lead_gen_spend": 3000,
"lead_gen_conversions": 20,
"lead_gen_cpa": 150,
"target_audience_size": 450000,
"audience_match_rate": "85%"
}
},
"historical_context": {
"last_quarter_performance": {
"google_ads": {
"spend": 28000,
"conversions": 175,
"cpa": 160
},
"meta_ads": {
"spend": 18000,
"conversions": 108,
"cpa": 166.67
},
"linkedin_ads": {
"spend": 9000,
"conversions": 50,
"cpa": 180
}
},
"notes": "Meta has degraded significantly (CPA up 26%), Google and LinkedIn relatively stable"
},
"constraints": {
"google_minimum": 25000,
"google_maximum": null,
"meta_minimum": 10000,
"meta_maximum": null,
"linkedin_minimum": 5000,
"linkedin_maximum": 15000,
"brand_carveout": 8000,
"new_platform_interest": [
"Microsoft Ads",
"TikTok (skeptical for B2B)"
]
},
"goals": {
"primary": "Reduce blended CPA while maintaining volume",
"target_conversions": "330+ demos/month",
"target_cpa": 150,
"secondary": "Test Microsoft Ads as potential Google supplement"
},
"attribution": {
"google": "Data-driven attribution",
"meta": "7-day click, 1-day view",
"linkedin": "30-day click",
"notes": "Attribution windows differ - LinkedIn may overclaim relative to others"
}
}
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{
"summary": {
"total_budget": 60000,
"current_conversions": 330,
"current_blended_cpa": 181.82,
"target_cpa": 150,
"gap": "+21%",
"primary_recommendation": "Shift budget from underperforming Meta prospecting to Google Brand and Microsoft Ads test",
"expected_improvement": "-12% CPA (from $182 to $160)"
},
"current_state_analysis": {
"performance_by_platform": [
{
"platform": "Google Ads",
"spend": 30000,
"conversions": 180,
"cpa": 166.67,
"vs_target": "+11%",
"percent_of_total": "50%",
"trend": "stable",
"efficiency_rank": 1
},
{
"platform": "LinkedIn Ads",
"spend": 10000,
"conversions": 55,
"cpa": 181.82,
"vs_target": "+21%",
"percent_of_total": "17%",
"trend": "stable",
"efficiency_rank": 2
},
{
"platform": "Meta Ads",
"spend": 20000,
"conversions": 95,
"cpa": 210.53,
"vs_target": "+40%",
"percent_of_total": "33%",
"trend": "declining",
"efficiency_rank": 3
}
],
"blended_metrics": {
"total_spend": 60000,
"total_conversions": 330,
"blended_cpa": 181.82,
"vs_target": "+21%"
},
"efficiency_ranking": "Google Brand ($107) > LinkedIn Lead Gen ($150) > Meta Retargeting ($150) > Google Non-Brand ($208) > LinkedIn Sponsored ($200) > Meta Prospecting ($255) > Google PMax ($212)"
},
"scaling_headroom_assessment": {
"google_ads": {
"signal": "68% impression share, 2 budget-limited campaigns",
"headroom": "HIGH",
"notes": "Clear room to scale, especially brand and efficient non-brand segments",
"marginal_cpa_estimate": "Similar to current at +$5K, slight increase at +$10K"
},
"meta_ads": {
"signal": "Frequency 2.8, declining performance trend",
"headroom": "LOW",
"notes": "Creative fatigue evident, scaling would likely worsen CPA further",
"marginal_cpa_estimate": "Expect 15-25% CPA increase on incremental spend"
},
"linkedin_ads": {
"signal": "Frequency 3.5, 450K audience at 85% match",
"headroom": "LOW-MEDIUM",
"notes": "Audience nearing saturation, can absorb small increase but limited",
"marginal_cpa_estimate": "Expect 10-15% CPA increase beyond current spend"
}
},
"sub_channel_analysis": {
"winners_scale": [
{
"channel": "Google - Brand",
"spend": 8000,
"cpa": 106.67,
"vs_target": "-29%",
"action": "Increase to capture more branded demand"
},
{
"channel": "LinkedIn - Lead Gen Forms",
"spend": 3000,
"cpa": 150,
"vs_target": "At target",
"action": "Maintain, at capacity for format"
},
{
"channel": "Meta - Retargeting",
"spend": 6000,
"cpa": 150,
"vs_target": "At target",
"action": "Maintain, efficient for retargeting role"
}
],
"optimize_fix": [
{
"channel": "Google - Non-Brand",
"spend": 15000,
"cpa": 208.33,
"vs_target": "+39%",
"action": "Optimize keywords/bids before scaling"
},
{
"channel": "LinkedIn - Sponsored Content",
"spend": 7000,
"cpa": 200,
"vs_target": "+33%",
"action": "Test new creative, consider audience refresh"
}
],
"reduce_cut": [
{
"channel": "Meta - Prospecting",
"spend": 14000,
"cpa": 254.55,
"vs_target": "+70%",
"action": "Reduce significantly until creative refreshed"
},
{
"channel": "Google - PMax",
"spend": 7000,
"cpa": 212.12,
"vs_target": "+41%",
"action": "Reduce or pause, reallocate to search"
}
]
},
"recommended_allocation": {
"model_used": "Marginal Return Optimization with Constraints",
"rationale": "Shift from declining Meta prospecting to proven Google channels and test Microsoft Ads for additional search inventory",
"allocation": [
{
"platform": "Google Ads",
"current": 30000,
"recommended": 36000,
"change": "+6000",
"percent_change": "+20%",
"percent_of_total": "60%",
"sub_allocation": {
"brand": {
"current": 8000,
"recommended": 10000,
"change": "+2000"
},
"non_brand": {
"current": 15000,
"recommended": 18000,
"change": "+3000"
},
"pmax": {
"current": 7000,
"recommended": 4000,
"change": "-3000"
},
"microsoft_import": {
"current": 0,
"recommended": 4000,
"change": "+4000"
}
}
},
{
"platform": "Meta Ads",
"current": 20000,
"recommended": 12000,
"change": "-8000",
"percent_change": "-40%",
"percent_of_total": "20%",
"sub_allocation": {
"prospecting": {
"current": 14000,
"recommended": 6000,
"change": "-8000"
},
"retargeting": {
"current": 6000,
"recommended": 6000,
"change": "0"
}
}
},
{
"platform": "LinkedIn Ads",
"current": 10000,
"recommended": 8000,
"change": "-2000",
"percent_change": "-20%",
"percent_of_total": "13%",
"sub_allocation": {
"sponsored_content": {
"current": 7000,
"recommended": 5000,
"change": "-2000"
},
"lead_gen": {
"current": 3000,
"recommended": 3000,
"change": "0"
}
}
},
{
"platform": "Microsoft Ads",
"current": 0,
"recommended": 4000,
"change": "+4000",
"percent_change": "NEW",
"percent_of_total": "7%",
"notes": "Import top Google campaigns, test for 6-8 weeks"
}
],
"total_allocated": 60000,
"testing_budget": 4000,
"testing_percent": "7%"
},
"allocation_rationale": {
"increase_google": [
"Best performing platform overall ($167 blended CPA)",
"32% impression share headroom indicates scaling capacity",
"Budget-limited campaigns suggest incremental spend will convert",
"Brand campaigns extremely efficient at $107 CPA"
],
"decrease_meta": [
"CPA increased 26% quarter-over-quarter (from $167 to $211)",
"Prospecting at $255 CPA is 70% above target",
"Declining trend suggests creative/audience fatigue",
"Keep retargeting which remains efficient at $150 CPA"
],
"decrease_linkedin": [
"Frequency at 3.5 indicates audience saturation",
"Small audience (450K) limits scaling potential",
"Maintain efficient Lead Gen Forms, reduce costly Sponsored Content"
],
"add_microsoft": [
"Natural extension of Google search success",
"Typically 20-30% lower CPCs than Google",
"Easy import from existing Google campaigns",
"Tests hypothesis that search intent converts for this business"
]
},
"projected_outcomes": {
"at_recommended_allocation": {
"projected_conversions": {
"google": 225,
"meta": 52,
"linkedin": 42,
"microsoft": 22,
"total": 341
},
"projected_cpa": {
"google": 160,
"meta": 230.77,
"linkedin": 190.48,
"microsoft": 181.82,
"blended": 175.95
}
},
"comparison": {
"current_conversions": 330,
"projected_conversions": 341,
"change": "+3%",
"current_cpa": 181.82,
"projected_cpa": 175.95,
"change_cpa": "-3%"
},
"assumptions": [
"Google scales efficiently to +$6K based on impression share headroom",
"Meta prospecting CPA holds at reduced spend (fewer bad impressions)",
"Microsoft achieves similar efficiency to Google non-brand",
"LinkedIn small reduction doesn't significantly impact delivery"
],
"confidence": "Medium - Google scaling likely, Meta recovery and Microsoft performance are unknowns"
},
"scenario_comparison": {
"conservative": {
"description": "Minimal changes, maintain current structure with small optimizations",
"allocation": {
"google": 32000,
"meta": 18000,
"linkedin": 10000,
"microsoft": 0
},
"projected_cpa": 178,
"projected_conversions": 337,
"risk": "Low",
"upside": "Low"
},
"balanced": {
"description": "Recommended allocation - shift from Meta to Google + Microsoft test",
"allocation": {
"google": 36000,
"meta": 12000,
"linkedin": 8000,
"microsoft": 4000
},
"projected_cpa": 175.95,
"projected_conversions": 341,
"risk": "Medium",
"upside": "Medium"
},
"aggressive": {
"description": "Major shift to Google, minimal Meta, larger Microsoft test",
"allocation": {
"google": 42000,
"meta": 10000,
"linkedin": 5000,
"microsoft": 3000
},
"projected_cpa": 168,
"projected_conversions": 357,
"risk": "High",
"upside": "High"
}
},
"implementation_plan": {
"phase_1_week_1": {
"actions": [
"Reduce Google PMax from $7K to $4K",
"Reduce Meta Prospecting from $14K to $10K",
"Increase Google Brand from $8K to $10K",
"Set up Microsoft Ads account and import top Google campaigns"
],
"net_change": "Google: +$2K, Meta: -$4K (holding $2K for Microsoft launch)"
},
"phase_2_weeks_2_3": {
"actions": [
"Launch Microsoft Ads at $4K/month",
"Reduce Meta Prospecting from $10K to $6K",
"Increase Google Non-Brand from $15K to $18K",
"Reduce LinkedIn Sponsored Content from $7K to $5K"
],
"net_change": "Reach target allocation"
},
"phase_3_weeks_4_plus": {
"actions": [
"Monitor performance at new levels",
"Optimize Microsoft Ads based on initial data",
"If Meta creative is refreshed, consider testing reallocation",
"Fine-tune Google bid strategies for new budget levels"
]
},
"change_management_notes": [
"Don't reduce Meta more than $4K in first week - algorithm needs adjustment time",
"Google budget increases should be gradual to maintain quality",
"Microsoft will need 2-3 weeks in learning phase - don't judge too early",
"Weekly reviews for first month, then bi-weekly"
]
},
"monitoring_framework": {
"weekly_metrics": [
"CPA by platform vs projection",
"Spend pacing vs allocation",
"Google impression share trend",
"Meta frequency trend",
"Microsoft learning phase status"
],
"rebalancing_triggers": [
"Any platform CPA exceeds projection by >25% for 2 consecutive weeks",
"Google impression share doesn't improve despite budget increase",
"Microsoft CPA exceeds $250 after 4 weeks",
"Meta retargeting CPA spikes above $200"
],
"success_criteria_4_weeks": [
"Blended CPA below $180",
"Total conversions above 320",
"Google impression share above 75%",
"Microsoft generating >15 conversions at <$200 CPA"
]
},
"risks_and_mitigation": [
{
"risk": "Google can't absorb +$6K efficiently",
"likelihood": "Low",
"impact": "CPA rises, efficiency gains don't materialize",
"mitigation": "Monitor impression share weekly, pause increase if CPA rises >15%"
},
{
"risk": "Meta performance continues declining even at lower spend",
"likelihood": "Medium",
"impact": "Need further cuts, volume decreases",
"mitigation": "Prioritize creative refresh; if no improvement in 4 weeks, cut to retargeting only"
},
{
"risk": "Microsoft Ads doesn't perform for B2B SaaS",
"likelihood": "Medium",
"impact": "$4K/month inefficiently spent",
"mitigation": "6-week test with clear kill criteria ($250 CPA threshold); easy to reallocate to Google"
},
{
"risk": "LinkedIn audience fully exhausted",
"likelihood": "Medium",
"impact": "Conversions drop faster than expected",
"mitigation": "Test new audience segments, consider reducing further and reallocating to Google"
}
],
"next_steps": [
"Review and approve recommended allocation",
"Set up Microsoft Ads account (can use Google import)",
"Brief creative team on Meta prospecting refresh need",
"Implement Phase 1 changes by end of week",
"Schedule weekly review for first month"
]
}
ReadyCross-Platform Budget Allocator
MarkdownUTF-8